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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ CvT model configuration""" | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| CVT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json", | |
| # See all Cvt models at https://huggingface.co/models?filter=cvt | |
| } | |
| class CvtConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT model | |
| according to the specified arguments, defining the model architecture. Instantiating a configuration with the | |
| defaults will yield a similar configuration to that of the CvT | |
| [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| num_channels (`int`, *optional*, defaults to 3): | |
| The number of input channels. | |
| patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`): | |
| The kernel size of each encoder's patch embedding. | |
| patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`): | |
| The stride size of each encoder's patch embedding. | |
| patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`): | |
| The padding size of each encoder's patch embedding. | |
| embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`): | |
| Dimension of each of the encoder blocks. | |
| num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`): | |
| Number of attention heads for each attention layer in each block of the Transformer encoder. | |
| depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`): | |
| The number of layers in each encoder block. | |
| mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`): | |
| Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the | |
| encoder blocks. | |
| attention_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`): | |
| The dropout ratio for the attention probabilities. | |
| drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`): | |
| The dropout ratio for the patch embeddings probabilities. | |
| drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`): | |
| The dropout probability for stochastic depth, used in the blocks of the Transformer encoder. | |
| qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`): | |
| The bias bool for query, key and value in attentions | |
| cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`): | |
| Whether or not to add a classification token to the output of each of the last 3 stages. | |
| qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`): | |
| The projection method for query, key and value Default is depth-wise convolutions with batch norm. For | |
| Linear projection use "avg". | |
| kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`): | |
| The kernel size for query, key and value in attention layer | |
| padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`): | |
| The padding size for key and value in attention layer | |
| stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`): | |
| The stride size for key and value in attention layer | |
| padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`): | |
| The padding size for query in attention layer | |
| stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`): | |
| The stride size for query in attention layer | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-6): | |
| The epsilon used by the layer normalization layers. | |
| Example: | |
| ```python | |
| >>> from transformers import CvtConfig, CvtModel | |
| >>> # Initializing a Cvt msft/cvt style configuration | |
| >>> configuration = CvtConfig() | |
| >>> # Initializing a model (with random weights) from the msft/cvt style configuration | |
| >>> model = CvtModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "cvt" | |
| def __init__( | |
| self, | |
| num_channels=3, | |
| patch_sizes=[7, 3, 3], | |
| patch_stride=[4, 2, 2], | |
| patch_padding=[2, 1, 1], | |
| embed_dim=[64, 192, 384], | |
| num_heads=[1, 3, 6], | |
| depth=[1, 2, 10], | |
| mlp_ratio=[4.0, 4.0, 4.0], | |
| attention_drop_rate=[0.0, 0.0, 0.0], | |
| drop_rate=[0.0, 0.0, 0.0], | |
| drop_path_rate=[0.0, 0.0, 0.1], | |
| qkv_bias=[True, True, True], | |
| cls_token=[False, False, True], | |
| qkv_projection_method=["dw_bn", "dw_bn", "dw_bn"], | |
| kernel_qkv=[3, 3, 3], | |
| padding_kv=[1, 1, 1], | |
| stride_kv=[2, 2, 2], | |
| padding_q=[1, 1, 1], | |
| stride_q=[1, 1, 1], | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.num_channels = num_channels | |
| self.patch_sizes = patch_sizes | |
| self.patch_stride = patch_stride | |
| self.patch_padding = patch_padding | |
| self.embed_dim = embed_dim | |
| self.num_heads = num_heads | |
| self.depth = depth | |
| self.mlp_ratio = mlp_ratio | |
| self.attention_drop_rate = attention_drop_rate | |
| self.drop_rate = drop_rate | |
| self.drop_path_rate = drop_path_rate | |
| self.qkv_bias = qkv_bias | |
| self.cls_token = cls_token | |
| self.qkv_projection_method = qkv_projection_method | |
| self.kernel_qkv = kernel_qkv | |
| self.padding_kv = padding_kv | |
| self.stride_kv = stride_kv | |
| self.padding_q = padding_q | |
| self.stride_q = stride_q | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |